In sponslerdb/vsearchR:

Load vsearch output

trnL_PH_pollen_2018 <- load_blast6("~/vsearchr/inst/extdata/trnL_vsearch_philly_2018.output/pollen/")
ITS1_PH_pollen_2018 <- load_blast6("~/vsearchr/inst/extdata/ITS1_vsearch_philly_2018.output/pollen/")
ITS2_PH_pollen_2018 <- load_blast6("~/vsearchr/inst/extdata/ITS2_vsearch_philly_2018.output/pollen/")
write_csv(trnL_PH_pollen_2018, "~/vsearchr/inst/extdata/trnL_PH_pollen_2018.csv")
write_csv(ITS1_PH_pollen_2018, "~/vsearchr/inst/extdata/ITS1_PH_pollen_2018.csv")
write_csv(ITS2_PH_pollen_2018, "~/vsearchr/inst/extdata/ITS2_PH_pollen_2018.csv")
trnL_PH_pollen_2018 <- read_csv("~/vsearchr/inst/extdata/trnL_PH_pollen_2018.csv", col_names = TRUE)
ITS1_PH_pollen_2018 <- read_csv("~/vsearchr/inst/extdata/ITS1_PH_pollen_2018.csv", col_names = TRUE)
ITS2_PH_pollen_2018 <- read_csv("~/vsearchr/inst/extdata/ITS2_PH_pollen_2018.csv", col_names = TRUE)

Load taxonomy tables

trnL_PH_tax <- load_MTXA("~/vsearchr/inst/extdata/trnL_PH_Amplicons2.tax")
ITS1_PH_tax <- load_MTXA("~/vsearchr/inst/extdata/ITS1_PH_Amplicons.tax")
ITS2_PH_tax <- load_MTXA("~/vsearchr/inst/extdata/ITS2_PH_Amplicons.tax")

Join vsearch output to taxonomy tables

trnL_PH_pollen_2018_join <- tax_join(trnL_PH_pollen_2018, trnL_PH_tax, min_id = 97.0, min_len = 150)
ITS1_PH_pollen_2018_join <- tax_join(ITS1_PH_pollen_2018, ITS1_PH_tax, min_id = 95.0, min_len = 300)
ITS2_PH_pollen_2018_join <- tax_join(ITS2_PH_pollen_2018, ITS2_PH_tax, min_id = 95.0, min_len = 300)
write_csv(trnL_PH_pollen_2018_join, "~/vsearchr/inst/extdata/trnL_PH_pollen_join_2018.csv")
write_csv(ITS1_PH_pollen_2018_join, "~/vsearchr/inst/extdata/ITS1_PH_pollen_join_2018.csv")
write_csv(ITS2_PH_pollen_2018_join, "~/vsearchr/inst/extdata/ITS2_PH_pollen_join_2018.csv")
trnL_PH_pollen_2018_join <- read_csv("~/vsearchr/inst/extdata/trnL_PH_pollen_join_2018.csv", col_names = TRUE)
ITS1_PH_pollen_2018_join <- read_csv("~/vsearchr/inst/extdata/ITS1_PH_pollen_join_2018.csv", col_names = TRUE)
ITS2_PH_pollen_2018_join <- read_csv("~/vsearchr/inst/extdata/ITS2_PH_pollen_join_2018.csv", col_names = TRUE)

Evaluate coverage

trnL_cov <- trnL_PH_pollen_2018 %>%
  group_by(sample) %>%
  summarize(reads = n())

ITS1_cov <- ITS1_PH_pollen_2018 %>%
  group_by(sample) %>%
  summarize(reads = n())

ITS2_cov <- ITS2_PH_pollen_2018 %>%
  group_by(sample) %>%
  summarize(reads = n())

cov <- full_join(trnL_cov, ITS1_cov, by = "sample") %>%
  full_join(ITS2_cov, by = "sample") %>%
  select(sample, trnL = 2, ITS1 = 3, ITS2 = 4)

write_csv(cov, "~/vsearchr/inst/extdata/output_2018/pollen_2018_coverage")

Tally genera by sample

trnL_PH_pollen_tally <- tally_gen(trnL_PH_pollen_2018_join)
ITS1_PH_pollen_tally <- tally_gen(ITS1_PH_pollen_2018_join)
ITS2_PH_pollen_tally <- tally_gen(ITS2_PH_pollen_2018_join)

Add sample metadata

trnL_PH_pollen_final <- add_meta(trnL_PH_pollen_tally, "~/vsearchr/inst/extdata/PH_2018_key.csv")
ITS1_PH_pollen_final <- add_meta(ITS1_PH_pollen_tally, "~/vsearchr/inst/extdata/PH_2018_key.csv")
ITS2_PH_pollen_final <- add_meta(ITS2_PH_pollen_tally, "~/vsearchr/inst/extdata/PH_2018_key.csv")

Create consensus data sets, create genus summary fields

PH_pollen_consensus <- consensus_xyz_gen(trnL_PH_pollen_final, ITS1_PH_pollen_final, ITS2_PH_pollen_final,
                                         min_prop = 0.0005) %>%
  add_meta("~/vsearchr/inst/extdata/PH_2018_key.csv") %>%
  mutate(period = case_when(date > "2018-05-01" & date < "2018-06-01" ~ "May",
                            date > "2018-06-01" & date < "2018-07-01" ~ "June",
                            date > "2018-07-01" & date < "2018-08-01" ~ "July",
                            date > "2018-08-01" & date < "2018-09-01" ~ "Aug",
                            date > "2018-09-01" & date < "2018-10-01" ~ "Sept",
                            date > "2018-10-01" & date < "2018-11-01" ~ "Oct",
                            is.na(date) & site == "Frankford" ~ "Sept",
                            is.na(date) & site == "Mayfair" ~ "Sept"))

PH_pollen_consensus_May <- filter(PH_pollen_consensus, period == "May")
PH_pollen_consensus_June <- filter(PH_pollen_consensus, period == "June")
PH_pollen_consensus_July <- filter(PH_pollen_consensus, period == "July")
PH_pollen_consensus_Aug <- filter(PH_pollen_consensus, period == "Aug")
PH_pollen_consensus_Sept <- filter(PH_pollen_consensus, period == "Sept")
PH_pollen_consensus_Oct <- filter(PH_pollen_consensus, period == "Oct")

PH_pollen_genus_summary <- PH_pollen_consensus %>%
  group_by(genus) %>%
  summarize(gen_freq = n(),
            max_prop = max(scaled_prop))

PH_pollen_genus_summary_May <- PH_pollen_consensus_May %>%
  group_by(genus) %>%
  summarize(gen_freq = n(),
            mean_prop = sum(scaled_prop)/12,
            max_prop = max(scaled_prop)) 

PH_pollen_genus_summary_June <- PH_pollen_consensus_June %>%
  group_by(genus) %>%
  summarize(gen_freq = n(),
            mean_prop = sum(scaled_prop)/12,
            max_prop = max(scaled_prop)) 

PH_pollen_genus_summary_July <- PH_pollen_consensus_July %>%
  group_by(genus) %>%
  summarize(gen_freq = n(),
            mean_prop = sum(scaled_prop)/12,
            max_prop = max(scaled_prop)) 

PH_pollen_genus_summary_Aug <- PH_pollen_consensus_Aug %>%
  group_by(genus) %>%
  summarize(gen_freq = n(),
            mean_prop = sum(scaled_prop)/12,
            max_prop = max(scaled_prop)) 

PH_pollen_genus_summary_Sept <- PH_pollen_consensus_Sept %>%
  group_by(genus) %>%
  summarize(gen_freq = n(),
            mean_prop = sum(scaled_prop)/12,
            max_prop = max(scaled_prop)) 

PH_pollen_genus_summary_Oct <- PH_pollen_consensus_Oct %>%
  group_by(genus) %>%
  summarize(gen_freq = n(),
            mean_prop = sum(scaled_prop)/12,
            max_prop = max(scaled_prop)) 

PH_pollen_final <- left_join(PH_pollen_consensus, PH_pollen_genus_summary, by = "genus")
PH_pollen_final_May <- left_join(PH_pollen_consensus_May, PH_pollen_genus_summary_May, by = "genus")
PH_pollen_final_June <- left_join(PH_pollen_consensus_June, PH_pollen_genus_summary_June, by = "genus")
PH_pollen_final_July <- left_join(PH_pollen_consensus_July, PH_pollen_genus_summary_July, by = "genus")
PH_pollen_final_Aug <- left_join(PH_pollen_consensus_Aug, PH_pollen_genus_summary_Aug, by = "genus")
PH_pollen_final_Sept <- left_join(PH_pollen_consensus_Sept, PH_pollen_genus_summary_Sept, by = "genus")
PH_pollen_final_Oct <- left_join(PH_pollen_consensus_Oct, PH_pollen_genus_summary_Oct, by = "genus")

write_csv(PH_pollen_final, "~/vsearchr/inst/extdata/output_2018/PH_2018_pollen.csv")
write_csv(PH_pollen_final_May, "~/vsearchr/inst/extdata/output_2018/PH_2018_pollen_may.csv")
write_csv(PH_pollen_final_June, "~/vsearchr/inst/extdata/output_2018/PH_2018_pollen_jun.csv")
write_csv(PH_pollen_final_July, "~/vsearchr/inst/extdata/output_2018/PH_2018_pollen_jul.csv")
write_csv(PH_pollen_final_Aug, "~/vsearchr/inst/extdata/output_2018/PH_2018_pollen_aug.csv")
write_csv(PH_pollen_final_Sept, "~/vsearchr/inst/extdata/output_2018/PH_2018_pollen_sep.csv")
write_csv(PH_pollen_final_Oct, "~/vsearchr/inst/extdata/output_2018/PH_2018_pollen_oct.csv")

#PH_pollen_final <- read_csv("~/vsearchr/inst/extdata/output_2018/PH_2018_pollen.csv")

Plot data

trnL only

ggplot(filter(trnL_PH_pollen_final, gen_prop >= 0.01), aes(x = date, y = genus, fill = gen_prop)) +
  geom_tile(width = 32, color = "white") +
  scale_fill_gradient(low = "gray95", high = "purple") +
  theme_bw(12) +
  ylab("Genus") +
  xlab("Sample") +
  labs(fill = "Proportional\nabundance") +
  facet_grid(~site) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

ITS1 only

ggplot(filter(ITS1_PH_pollen_final, gen_prop >= 0.01), aes(x = date, y = genus, fill = gen_prop)) +
  geom_tile(width = 12, color = "white") +
  scale_fill_gradient(low = "gray95", high = "purple") +
  theme_bw(12) +
  ylab("Genus") +
  xlab("Sample") +
  labs(fill = "Proportional\nabundance") +
  facet_grid(~site) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

ITS2 only

ggplot(filter(ITS2_PH_pollen_final, gen_prop >= 0.01), aes(x = date, y = genus, fill = gen_prop)) +
  geom_tile(width = 12, color = "white") +
  scale_fill_gradient(low = "gray95", high = "purple") +
  theme_bw(12) +
  ylab("Genus") +
  xlab("Sample") +
  labs(fill = "Proportional\nabundance") +
  facet_grid(~site) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

2-of-3 consensus

ggplot(PH_pollen_final, aes(x = date, y = reorder(genus, gen_freq), fill = scaled_prop)) +
  geom_tile(width = 30, color = "gray40") +
  scale_fill_gradient(low = "gray95", high = "purple") +
  theme_bw(4) +
  ylab("Genus") +
  xlab("Sample") +
  labs(fill = "Proportional\nabundance") +
  facet_grid(~site) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("./inst/extdata/output_2018/pollen_2018.pdf")

ggplot(PH_pollen_final_May, aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
  geom_tile(color = "gray40") +
  scale_fill_gradient(low = "gray95", high = "purple") +
  theme_bw(12) +
  ylab("Genus") +
  xlab("Site") +
  labs(fill = "Proportional\nabundance") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("./inst/extdata/output_2018/pollen_2018_may.pdf", width = 6, height = 8)

ggplot(PH_pollen_final_June, aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
  geom_tile(color = "gray40") +
  scale_fill_gradient(low = "gray95", high = "purple") +
  theme_bw(12) +
  ylab("Genus") +
  xlab("Site") +
  labs(fill = "Proportional\nabundance") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("./inst/extdata/output_2018/pollen_2018_june.pdf", width = 6, height = 8)


ggplot(PH_pollen_final_July, aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
  geom_tile(color = "gray40") +
  scale_fill_gradient(low = "gray95", high = "purple") +
  theme_bw(12) +
  ylab("Genus") +
  xlab("Site") +
  labs(fill = "Proportional\nabundance") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("./inst/extdata/output_2018/pollen_2018_july.pdf", width = 6, height = 8)


ggplot(PH_pollen_final_Aug, aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
  geom_tile(color = "gray40") +
  scale_fill_gradient(low = "gray95", high = "purple") +
  theme_bw(12) +
  ylab("Genus") +
  xlab("Site") +
  labs(fill = "Proportional\nabundance") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("./inst/extdata/output_2018/pollen_2018_aug.pdf", width = 6, height = 8)


ggplot(PH_pollen_final_Sept, aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
  geom_tile(color = "gray40") +
  scale_fill_gradient(low = "gray95", high = "purple") +
  theme_bw(12) +
  ylab("Genus") +
  xlab("Site") +
  labs(fill = "Proportional\nabundance") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("./inst/extdata/output_2018/pollen_2018_sept.pdf", width = 6, height = 8)


ggplot(PH_pollen_final_Oct, aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
  geom_tile(color = "gray40") +
  scale_fill_gradient(low = "gray95", high = "purple") +
  theme_bw(12) +
  ylab("Genus") +
  xlab("Site") +
  labs(fill = "Proportional\nabundance") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("./inst/extdata/output_2018/pollen_2018_oct.pdf", width = 6, height = 8)

Truncated for presentation

ggplot(filter(PH_pollen_final_May, max_prop >= 0.05), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
  geom_tile(color = "gray40") +
  scale_fill_gradient(low = "gray95", high = "purple") +
  theme_bw(12) +
  ylab("Genus") +
  xlab("Site") +
  labs(fill = "Proportional\nabundance") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("~/Desktop/may2018_pollen.png", device = "png")

ggplot(filter(PH_pollen_final_June, max_prop >= 0.05), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
  geom_tile(color = "gray40") +
  scale_fill_gradient(low = "gray95", high = "purple") +
  theme_bw(12) +
  ylab("Genus") +
  xlab("Site") +
  labs(fill = "Proportional\nabundance") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("~/Desktop/June2018_pollen.png", device = "png")


ggplot(filter(PH_pollen_final_July, max_prop >= 0.05), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
  geom_tile(color = "gray40") +
  scale_fill_gradient(low = "gray95", high = "purple") +
  theme_bw(12) +
  ylab("Genus") +
  xlab("Site") +
  labs(fill = "Proportional\nabundance") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("~/Desktop/july2018_pollen.png", device = "png")


ggplot(filter(PH_pollen_final_Aug, max_prop >= 0.05), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
  geom_tile(color = "gray40") +
  scale_fill_gradient(low = "gray95", high = "purple") +
  theme_bw(12) +
  ylab("Genus") +
  xlab("Site") +
  labs(fill = "Proportional\nabundance") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("~/Desktop/august2018_pollen.png", device = "png")


ggplot(filter(PH_pollen_final_Sept, max_prop >= 0.05), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
  geom_tile(color = "gray40") +
  scale_fill_gradient(low = "gray95", high = "purple") +
  theme_bw(12) +
  ylab("Genus") +
  xlab("Site") +
  labs(fill = "Proportional\nabundance") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("~/Desktop/sept2018_pollen.png", device = "png")


ggplot(filter(PH_pollen_final_Oct, max_prop >= 0.05), aes(x = site, y = reorder(genus, mean_prop), fill = scaled_prop)) +
  geom_tile(color = "gray40") +
  scale_fill_gradient(low = "gray95", high = "purple") +
  theme_bw(12) +
  ylab("Genus") +
  xlab("Site") +
  labs(fill = "Proportional\nabundance") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("~/Desktop/october2018_pollen.png", device = "png")

Woody vs herb analysis

pollen_may_form <- PH_pollen_final_May %>%
  ungroup() %>%
  select(genus, mean_prop) %>%
  unique() %>%
  arrange(-mean_prop) %>%
  mutate(cum_prop = cumsum(mean_prop)) %>%
  filter(cum_prop <= 0.90) %>%
  mutate(
    form = case_when(
      genus == "Salix" ~ "woody",
      genus == "Acer" ~ "woody",
      genus == "Quercus" ~ "woody",
      genus == "Malus" ~ "woody",
      genus == "Platanus" ~ "woody",
      genus == "Fraxinus" ~ "woody",
      genus == "Photinia" ~ "woody",
      genus == "Paulownia" ~ "woody",
      genus == "Taraxacum" ~ "herb",
      genus == "Cercis" ~ "woody",
      genus == "Halesia" ~ "woody",
      genus == "Morus" ~ "woody",
      genus == "Pyrus" ~ "woody",
      genus == "Cornus" ~ "woody",
      genus == "Crataegus" ~ "woody",
      genus == "Magnolia" ~ "woody",
      genus == "Viburnum" ~ "woody",
      genus == "Ornithogalum" ~ "herb",
      genus == "Brassica" ~ "herb"))

pollen_jun_form <- PH_pollen_final_June %>%
  ungroup() %>%
  select(genus, mean_prop) %>%
  unique() %>%
  arrange(-mean_prop) %>%
  mutate(cum_prop = cumsum(mean_prop)) %>%
  filter(cum_prop <= 0.90) %>%
  mutate(form = case_when(
    genus == "Trifolium" ~ "herb",
    genus == "Rhus" ~ "woody",
    genus == "Gleditsia" ~ "woody",
    genus == "Magnolia" ~ "woody",
    genus == "Melilotus" ~ "herb",
    genus == "Sambucus" ~ "woody",
    genus == "Acer" ~ "woody",
    genus == "Securigera" ~ "herb",
    genus == "Hydrangea" ~ "woody",
    genus == "Ligustrum" ~ "woody",
    genus == "Viburnum" ~ "woody",
    genus == "Castanea" ~ "woody",
    genus == "Plantago" ~ "herb",
    genus == "Spiraea" ~ "woody",
    genus == "Salix" ~ "woody",
    genus == "Tilia" ~ "woody",
    genus == "Vitis" ~ "woody",
    genus == "Crataegus" ~ "woody",
    genus == "Cornus" ~ "woody",
    genus == "Ailanthus" ~ "woody",
    genus == "Lonicera" ~ "woody",
    genus == "Fraxinus" ~ "woody",
    genus == "Syringa" ~ "woody",
    genus == "Cirsium" ~ "herb",
    genus == "Phedimus" ~ "herb",
    genus == "Ilex" ~ "woody",
    genus == "Amorpha" ~ "woody",
    genus == "Sedum" ~ "herb",
    genus == "Trigonella" ~ "herb",
    genus == "Pinus" ~ "woody"))

pollen_jul_form <- PH_pollen_final_July %>%
  ungroup() %>%
  select(genus, mean_prop) %>%
  unique() %>%
  arrange(-mean_prop) %>%
  mutate(cum_prop = cumsum(mean_prop)) %>%
  filter(cum_prop <= 0.90) %>%
  mutate(form = case_when(
    genus == "Trifolium" ~ "herb",
    genus == "Lagerstroemia" ~ "woody",
    genus == "Parthenocissus" ~ "woody",
    genus == "Verbascum" ~ "herb",
    genus == "Aralia" ~ "woody",
    genus == "Plantago" ~ "herb",
    genus == "Hydrangea" ~ "woody",
    genus == "Magnolia" ~ "woody",
    genus == "Melilotus" ~ "herb",
    genus == "Arctium" ~ "herb",
    genus == "Campsis" ~ "woody"))

pollen_aug_form <- PH_pollen_final_Aug %>%
  ungroup() %>%
  select(genus, mean_prop) %>%
  unique() %>%
  arrange(-mean_prop) %>%
  mutate(cum_prop = cumsum(mean_prop)) %>%
  filter(cum_prop <= 0.90) %>%
  mutate(form = case_when(
    genus == "Aralia" ~ "woody",
    genus == "Lagerstroemia" ~ "woody",
    genus == "Styphnolobium" ~ "woody",
    genus == "Melilotus" ~ "herb",
    genus == "Trifolium" ~ "herb",
    genus == "Hydrangea" ~ "woody",
    genus == "Plantago" ~ "herb",
    genus == "Koelreuteria" ~ "woody",
    genus == "Lythrum" ~ "herb",
    genus == "Cichorium" ~ "herb"))

pollen_sep_form <- PH_pollen_final_Sept %>%
  ungroup() %>%
  select(genus, mean_prop) %>%
  unique() %>%
  arrange(-mean_prop) %>%
  mutate(cum_prop = cumsum(mean_prop)) %>%
  filter(cum_prop <= 0.90) %>%
  mutate(form = case_when(
    genus == "Clematis" ~ "woody",
    genus == "Hedera" ~ "woody",
    genus == "Eupatorium" ~ "herb",
    genus == "Trifolium" ~ "herb",
    genus == "Lagerstroemia" ~ "woody",
    genus == "Polygonum" ~ "herb",
    genus == "Phragmites" ~ "herb",
    genus == "Fallopia" ~ "herb",
    genus == "Humulus" ~ "herb",
    genus == "Aralia" ~ "woody",
    genus == "Heterotheca" ~ "herb",
    genus == "Ambrosia" ~ "herb",
    genus == "Liriope" ~ "herb"))

pollen_oct_form <- PH_pollen_final_Oct %>%
  ungroup() %>%
  select(genus, mean_prop) %>%
  unique() %>%
  arrange(-mean_prop) %>%
  mutate(cum_prop = cumsum(mean_prop)) %>%
  filter(cum_prop <= 0.90) %>%
  mutate(form = case_when(
    genus == "Hedera" ~ "woody",
    genus == "Symphyotrichum" ~ "woody",
    genus == "Lycium" ~ "woody",
    genus == "Clematis" ~ "woody",
    genus == "Ageratina" ~ "herb",
    genus == "Dioscorea" ~ "herb",
    genus == "Liriope" ~ "herb",
    genus == "Melilotus" ~ "herb",
    genus == "Artemisia" ~ "herb",
    genus == "Solidago" ~ "herb",
    genus == "Capsicum" ~ "herb",
    genus == "Magnolia" ~ "woody",
    genus == "Lagerstroemia" ~ "woody",
    genus == "Trifolium" ~ "herb",
    genus == "Viburnum" ~ "woody")) 

ggplot(pollen_may_form, aes(reorder(genus, -mean_prop), mean_prop, fill = form)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_discrete_manual(aesthetics = "fill", values = c("gray60", "gray20")) +
  xlab("genus") +
  ylab("mean proportional abundance") +
  theme_void() +
  theme(legend.position = "none")
ggsave("~/vsearchr/inst/extdata/output_2018/pollen_may_WH.png")

ggplot(pollen_jun_form, aes(reorder(genus, -mean_prop), mean_prop, fill = form)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_discrete_manual(aesthetics = "fill", values = c("gray60", "gray20")) +
  xlab("genus") +
  ylab("mean proportional abundance") +
  theme_void() +
  theme(legend.position = "none")
ggsave("~/vsearchr/inst/extdata/output_2018/pollen_jun_WH.png")

ggplot(pollen_jul_form, aes(reorder(genus, -mean_prop), mean_prop, fill = form)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_discrete_manual(aesthetics = "fill", values = c("gray60", "gray20")) +
  xlab("genus") +
  ylab("mean proportional abundance") +
  theme_void() +
  theme(legend.position = "none")
ggsave("~/vsearchr/inst/extdata/output_2018/pollen_jul_WH.png")

ggplot(pollen_aug_form, aes(reorder(genus, -mean_prop), mean_prop, fill = form)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_discrete_manual(aesthetics = "fill", values = c("gray60", "gray20")) +
  xlab("genus") +
  ylab("mean proportional abundance") +
  theme_void() +
  theme(legend.position = "none")
ggsave("~/vsearchr/inst/extdata/output_2018/pollen_aug_WH.png")

ggplot(pollen_sep_form, aes(reorder(genus, -mean_prop), mean_prop, fill = form)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_discrete_manual(aesthetics = "fill", values = c("gray60", "gray20")) +
  xlab("genus") +
  ylab("mean proportional abundance") +
  theme_void() +
  theme(legend.position = "none")
ggsave("~/vsearchr/inst/extdata/output_2018/pollen_sep_WH.png")

ggplot(pollen_oct_form, aes(reorder(genus, -mean_prop), mean_prop, fill = form)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_discrete_manual(aesthetics = "fill", values = c("gray60", "gray20")) +
  xlab("genus") +
  ylab("mean proportional abundance") +
  theme_void() +
  theme(legend.position = "none")
ggsave("~/vsearchr/inst/extdata/output_2018/pollen_oct_WH.png")

Habitat affinity analysis, made up by me, based on my field experience

pollen_may_class <- PH_pollen_final_May %>%
  ungroup() %>%
  select(genus, mean_prop) %>%
  unique() %>%
  arrange(-mean_prop) %>%
  mutate(cum_prop = cumsum(mean_prop)) %>%
  filter(cum_prop <= 0.90) %>%
  mutate(
    class = case_when(
      genus == "Salix" ~ "seminatural",
      genus == "Acer" ~ "seminatural",
      genus == "Quercus" ~ "seminatural",
      genus == "Malus" ~ "ornamental",
      genus == "Platanus" ~ "ornamental",
      genus == "Fraxinus" ~ "seminatural",
      genus == "Photinia" ~ "ornamental",
      genus == "Paulownia" ~ "ruderal",
      genus == "Taraxacum" ~ "ruderal",
      genus == "Cercis" ~ "ornamental",
      genus == "Halesia" ~ "ornamental",
      genus == "Morus" ~ "ruderal",
      genus == "Pyrus" ~ "ornamental",
      genus == "Cornus" ~ "ornamental",
      genus == "Crataegus" ~ "seminatural",
      genus == "Magnolia" ~ "ornamental",
      genus == "Viburnum" ~ "ornamental",
      genus == "Ornithogalum" ~ "ruderal",
      genus == "Brassica" ~ "ruderal"))

pollen_jun_class <- PH_pollen_final_June %>%
  ungroup() %>%
  select(genus, mean_prop) %>%
  unique() %>%
  arrange(-mean_prop) %>%
  mutate(cum_prop = cumsum(mean_prop)) %>%
  filter(cum_prop <= 0.90) %>%
  mutate(class = case_when(
    genus == "Trifolium" ~ "ruderal",
    genus == "Rhus" ~ "ruderal",
    genus == "Gleditsia" ~ "seminatural",
    genus == "Magnolia" ~ "ornamental",
    genus == "Melilotus" ~ "ruderal",
    genus == "Sambucus" ~ "ornamental",
    genus == "Acer" ~ "seminatural",
    genus == "Securigera" ~ "ruderal",
    genus == "Hydrangea" ~ "ornamental",
    genus == "Ligustrum" ~ "ornamental",
    genus == "Viburnum" ~ "ornamental",
    genus == "Castanea" ~ "ornamental",
    genus == "Plantago" ~ "ruderal",
    genus == "Spiraea" ~ "ornamental",
    genus == "Salix" ~ "seminatural",
    genus == "Tilia" ~ "ornamental",
    genus == "Vitis" ~ "ruderal",
    genus == "Crataegus" ~ "seminatural",
    genus == "Cornus" ~ "ornamental",
    genus == "Ailanthus" ~ "ruderal",
    genus == "Lonicera" ~ "ruderal",
    genus == "Fraxinus" ~ "seminatural",
    genus == "Syringa" ~ "ornamental",
    genus == "Cirsium" ~ "ruderal",
    genus == "Phedimus" ~ "ornamental",
    genus == "Ilex" ~ "ornamental",
    genus == "Amorpha" ~ "ruderal",
    genus == "Sedum" ~ "ornamental",
    genus == "Trigonella" ~ "ornamental",
    genus == "Pinus" ~ "seminatural"))

pollen_jul_class <- PH_pollen_final_July %>%
  ungroup() %>%
  select(genus, mean_prop) %>%
  unique() %>%
  arrange(-mean_prop) %>%
  mutate(cum_prop = cumsum(mean_prop)) %>%
  filter(cum_prop <= 0.90) %>%
  mutate(class = case_when(
    genus == "Trifolium" ~ "ruderal",
    genus == "Lagerstroemia" ~ "ornamental",
    genus == "Parthenocissus" ~ "ruderal",
    genus == "Verbascum" ~ "ruderal",
    genus == "Aralia" ~ "seminatural",
    genus == "Plantago" ~ "ruderal",
    genus == "Hydrangea" ~ "ornamental",
    genus == "Magnolia" ~ "ornamental",
    genus == "Melilotus" ~ "ruderal",
    genus == "Arctium" ~ "ruderal",
    genus == "Campsis" ~ "seminatural"))

pollen_aug_class <- PH_pollen_final_Aug %>%
  ungroup() %>%
  select(genus, mean_prop) %>%
  unique() %>%
  arrange(-mean_prop) %>%
  mutate(cum_prop = cumsum(mean_prop)) %>%
  filter(cum_prop <= 0.90) %>%
  mutate(class = case_when(
    genus == "Aralia" ~ "seminatural",
    genus == "Lagerstroemia" ~ "ornamental",
    genus == "Styphnolobium" ~ "ornamental",
    genus == "Melilotus" ~ "ruderal",
    genus == "Trifolium" ~ "ruderal",
    genus == "Hydrangea" ~ "ornamental",
    genus == "Plantago" ~ "ruderal",
    genus == "Koelreuteria" ~ "ornamental",
    genus == "Lythrum" ~ "ruderal",
    genus == "Cichorium" ~ "ruderal"))

pollen_sep_class <- PH_pollen_final_Sept %>%
  ungroup() %>%
  select(genus, mean_prop) %>%
  unique() %>%
  arrange(-mean_prop) %>%
  mutate(cum_prop = cumsum(mean_prop)) %>%
  filter(cum_prop <= 0.90) %>%
  mutate(class = case_when(
    genus == "Clematis" ~ "ornamental",
    genus == "Hedera" ~ "ornamental",
    genus == "Eupatorium" ~ "ruderal",
    genus == "Trifolium" ~ "ruderal",
    genus == "Lagerstroemia" ~ "ornamental",
    genus == "Polygonum" ~ "ruderal",
    genus == "Phragmites" ~ "ruderal",
    genus == "Fallopia" ~ "ruderal",
    genus == "Humulus" ~ "ruderal",
    genus == "Aralia" ~ "seminatural",
    genus == "Heterotheca" ~ "ruderal",
    genus == "Ambrosia" ~ "ruderal",
    genus == "Liriope" ~ "ornamental"))

pollen_oct_class <- PH_pollen_final_Oct %>%
  ungroup() %>%
  select(genus, mean_prop) %>%
  unique() %>%
  arrange(-mean_prop) %>%
  mutate(cum_prop = cumsum(mean_prop)) %>%
  filter(cum_prop <= 0.90) %>%
  mutate(class = case_when(
    genus == "Hedera" ~ "ornamental",
    genus == "Symphyotrichum" ~ "ruderal",
    genus == "Lycium" ~ "ornamental",
    genus == "Clematis" ~ "ornamental",
    genus == "Ageratina" ~ "ruderal",
    genus == "Dioscorea" ~ "ruderal",
    genus == "Liriope" ~ "ornamental",
    genus == "Melilotus" ~ "ruderal",
    genus == "Artemisia" ~ "ruderal",
    genus == "Solidago" ~ "ruderal",
    genus == "Capsicum" ~ "ornamental",
    genus == "Magnolia" ~ "ornamental",
    genus == "Lagerstroemia" ~ "ornamental",
    genus == "Trifolium" ~ "ruderal",
    genus == "Viburnum" ~ "ornamental")) 

ggplot(pollen_may_class, aes(reorder(genus, -mean_prop), mean_prop, fill = class)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_discrete_manual(values = c("#F8766D", "#00BA38", "#619CFF"), aesthetics = "fill") +
  theme(axis.title.x = element_blank()) +
  ylab("mean proportional abundance")
ggsave("~/vsearchr/inst/extdata/output_2018/pollen_may_class.png")

ggplot(pollen_jun_class, aes(reorder(genus, -mean_prop), mean_prop, fill = class)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_discrete_manual(values = c("#F8766D", "#00BA38", "#619CFF"), aesthetics = "fill") +
  theme(axis.title.x = element_blank()) +
  ylab("mean proportional abundance")
ggsave("~/vsearchr/inst/extdata/output_2018/pollen_jun_class.png")

ggplot(pollen_jul_class, aes(reorder(genus, -mean_prop), mean_prop, fill = class)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_discrete_manual(values = c("#F8766D", "#00BA38", "#619CFF"), aesthetics = "fill") +
  theme(axis.title.x = element_blank()) +
  ylab("mean proportional abundance")
ggsave("~/vsearchr/inst/extdata/output_2018/pollen_jul_class.png")

ggplot(pollen_aug_class, aes(reorder(genus, -mean_prop), mean_prop, fill = class)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_discrete_manual(values = c("#F8766D", "#00BA38", "#619CFF"), aesthetics = "fill") +
  theme(axis.title.x = element_blank()) +
  ylab("mean proportional abundance")
ggsave("~/vsearchr/inst/extdata/output_2018/pollen_aug_class.png")

ggplot(pollen_sep_class, aes(reorder(genus, -mean_prop), mean_prop, fill = class)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_discrete_manual(values = c("#F8766D", "#00BA38"), aesthetics = "fill") +
  theme(axis.title.x = element_blank()) +
  ylab("mean proportional abundance")
ggsave("~/vsearchr/inst/extdata/output_2018/pollen_sep_class.png")

ggplot(pollen_oct_class, aes(reorder(genus, -mean_prop), mean_prop, fill = class)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_discrete_manual(values = c("#F8766D", "#00BA38"), aesthetics = "fill") +
  theme(axis.title.x = element_blank()) +
  ylab("mean proportional abundance")
ggsave("~/vsearchr/inst/extdata/output_2018/pollen_oct_class.png")